





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline






import math




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int)   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   0
							__UpperCamelCase      :  int       =   0
							while num > 0:
														__UpperCamelCase      :  List[Any]       =   num % 8
														__UpperCamelCase      :  Tuple       =   octal + (remainder * math.floor(math.pow(10		,    _lowerCamelCase)))
														counter += 1
														__UpperCamelCase      :  Optional[Any]       =   math.floor(num / 8)  # basically /= 8 without remainder if any
														# This formatting removes trailing '.0' from `octal`.
							return F'0o{int(_lowerCamelCase)}'




def       _SCREAMING_SNAKE_CASE   (						)   ->       None:






							'''simple docstring'''





							print("\n2 in octal is:")
							print(decimal_to_octal(2))  # = 2
							print("\n8 in octal is:")
							print(decimal_to_octal(8))  # = 10
							print("\n65 in octal is:")
							print(decimal_to_octal(65))  # = 101
							print("\n216 in octal is:")
							print(decimal_to_octal(216))  # = 330
							print("\n512 in octal is:")
							print(decimal_to_octal(512))  # = 1000
							print("\n")


if __name__ == "__main__":
		main()